U.S. patent number 5,412,325 [Application Number 08/173,534] was granted by the patent office on 1995-05-02 for phase noise measurement system and method.
This patent grant is currently assigned to Hughes Aircraft Company. Invention is credited to Clifford W. Meyers.
United States Patent |
5,412,325 |
Meyers |
May 2, 1995 |
Phase noise measurement system and method
Abstract
Three independent signal sources are used to statistically
derive the power spectral density of the phase noise content of
signals from each of them. This is accomplished by mixing each of
the signals two at a time (i.e., signal one with signal two, signal
one with signal three, and signal two with signal three) and
capturing the resultant difference signals, such as with a waveform
recorder, for example. A servo electronics loop is used to remove
the carrier and any long term signal drift from the resultant
difference signals. Statistical analysis is then used to compute
the composite power spectral densities of the the resultant
difference signals, and to solve for the individual power spectral
densities of the original signals. The present system and method
uses the mathematical relationships between the three sources that
have similar magnitudes of phase noise, to compute the power
spectral density of the noise content of signals from each source.
The present system and method requires a minimum of interconnect
hardware and only three inexpensive waveform recorders.
Furthermore, the size, weight, and cost of producing the present
phase noise test system is relatively low.
Inventors: |
Meyers; Clifford W. (Rancho
Palos Verdes, CA) |
Assignee: |
Hughes Aircraft Company (Los
Angeles, CA)
|
Family
ID: |
22632467 |
Appl.
No.: |
08/173,534 |
Filed: |
December 23, 1993 |
Current U.S.
Class: |
324/613; 702/106;
708/422 |
Current CPC
Class: |
G01R
29/26 (20130101) |
Current International
Class: |
G01R
29/00 (20060101); G01R 29/26 (20060101); G01R
027/00 () |
Field of
Search: |
;455/67.3,67.6,63
;324/613 ;364/728.03 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Wieder; Kenneth A.
Assistant Examiner: Solis; Jose M.
Attorney, Agent or Firm: Alkov; Leonard A. Denson-Low; Wanda
K.
Claims
What is claimed is:
1. A phase noise measurement system comprising:
a primary signal source for providing a first signal whose phase
noise is to be measured;
second and third signal sources for providing second and third
signals that each have substantially the same frequency and a
similar noise content as first signal provided by the primary
signal source;
mixing means for mixing each of the first, second, and third
signals two at a time to generate three respective difference
signals;
servo means for processing the three difference signals to remove
carrier signals and long term signal drift therefrom;
waveform recorder means coupled to the mixing means for capturing
the respective three difference signals;
processing means coupled to the waveform recorder means for
statistically analyzing the three difference signals to compute
composite power spectral densities therefor, and for computing
respective individual power spectral densities of the first, second
and third signals from the composite power spectral densities, and
hence determining the phase noise of the primary signal source.
2. The system of claim 1 wherein the mixing means comprises:
a plurality of mixers;
a plurality of signal splitters selectively coupled between the
signal sources and first inputs of the respective mixers;
a plurality of programmable phase shifters respectively coupled
between a selected splitter and second inputs of selected mixers;
and
a plurality of filters coupled to respective outputs of the
plurality of mixers.
3. The system of claim 2 wherein the waveform recorder means is
comprised of three waveform recorder channels.
4. The system of claim 3 further comprising a plurality of phase
locked loops.
5. A method of determining the phase noise of signals provided by a
primary signal source comprising:
providing a first signal from a primary signal source whose phase
noise is to be measured;
providing second and third signals from second and third signal
sources that each have substantially the same frequency and a
similar noise content as first signal provided by the primary
signal source;
mixing each of the first, second, and third signals two at a time
to generate three respective difference signals;
processing the three difference signals to remove residual carrier
and drift therefrom;
statistically analyzing the three difference signals to compute
composite power spectral densities therefor; and
computing respective individual power spectral densities of the
first, second and third signals from the composite power spectral
densities to determine the phase noise of the primary signal
source.
6. The method of claim 5 wherein the step of processing the three
difference signals to remove the residual carrier and drift
therefrom comprises the step of:
sweeping a plurality of phase shifters to remove the respective
residual carrier and drift from the three difference signals.
Description
BACKGROUND
The present invention relates generally to phase noise measurement
systems, and more particularly, to a phase noise measurement system
that uses three independent signal sources to statistically derive
the power spectral density of the phase noise content of each of
them.
Typical phase noise measurement test sets available in the
commercial market fall into two primary categories: a two
oscillator systems (or phase detector system) and a delay line
discriminator system. To clarify these systems, each is discussed
separately. The two oscillator system uses two sources (a unit
under test and a reference source) that are set to the same
frequency and that are in quadrature with respect to each other.
These signals are input to a double balanced mixer that is used as
a phase detector, and the resultant output is passed through a low
pass filter. The remaining signal is a low voltage DC signal that
is summed with an AC signal. The AC fluctuations are proportional
to the combined phase noise of the original two signal sources.
This AC signal is then fed into a spectrum analyzer and the power
spectrum is displayed to a user. The primary limiting factor of
this approach is the requirement that the reference source have
phase noise characteristics that are at least 10 dB better than the
source that is measured. In the case of phase noise measurements
for radar signal sources, these sources have very low phase noise,
and finding a reference that is superior can be very difficult or
impossible. Therefore, this method is primarily used for measuring
signal sources of higher noise content or for measuring phase noise
close to the carrier.
The delay line discriminator system does not require additional
reference sources. It uses the signal from the unit under test and
splits it into two signals. The signal in one pats is input into a
delay line whose output feeds a phase detector. The other signal is
fed directly to the phase detector. Phase detecting the delayed and
non-delayed signals together creates a discrimination effect which
produces a frequency modulated signal proportional to the signal's
inherent noise content. This FM noise signal is then inteegrated
and measured by a baseband spectrum analyzer. This system has
several limitations. Its sensitivity is proportional to the delay
time and the larger the delay time the greater the insertion loss.
This acts as a practical limitation for the sensitivity of the
system. Furthermore, the sensitivity degrades as 1/f.sup.2 as the
carrier is approached. Therefore, this technique is not useful
measuring very stable sources close to the carrier frequency.
Accordingly, it is an objective of the present invention to provide
for an improved phase noise measurement system that overcomes the
limitations of conventional systems.
SUMMARY OF THE INVENTION
The present system and method is used to determine the phase noise
of a primary signal source. The present system uses three
independent signal sources to statistically derive the power
spectral density of the phase noise content of signals from each of
them. This is accomplished by mixing each of the signals two at a
time (i.e., signal one with signal two, signal one with signal
three, and signal two with signal three) and measuring the
resultant difference signals, such as with a waveform recorder. A
servo electronics subsystem is used to remove the residual low
frequency products and any long term signal drift from the
difference signals, and the difference signals are then captured
via three channels of waveform recorders. Statistical analysis
routines are then used to compute the composite power spectral
densities of the difference signals, and to ultimately solve for
the individual power spectral densities of the original signals.
Thus, the phase noise of the primary signal source is
determined.
More particularly, the present invention comprises a phase noise
measurement system that includes a primary signal source for
providing a first signal whose phase noise is to be measured, and
second and third signal sources (for providing second and third
signals that each have substantially the same frequency and a
similar noise content as first signal provided by the primary
signal source). Mixing means are coupled to the three signal
sources for mixing each of the first, second, and third signals two
at a time to generate three respective difference signals. Servo
electronics are coupled to the mixing means for sensing residual
low frequency carrier artifacts and long term carrier drift and
removing them. Waveform recorder means are coupled to the mixing
means for capturing the respective magnitudes of the three
difference signals. Processing means are coupled to the waveform
recorder means for processing the three difference signals to
statistically analyze them to compute composite power spectral
densities therefor, and for computing respective individual power
spectral densities of the first, second and third signals from the
composite power spectral densities, and hence determining the phase
noise of the primary signal source.
The present invention also provides for method of determining the
phase noise of signals from a primary signal source. The method
comprises the following steps. Providing a first signal from a
primary signal source whose phase noise is to be measured.
Providing second and third signals from second and third signal
sources that each have substantially the same frequency and a
similar noise content as first signal provided by the primary
signal source. Mixing each of the first, second, and third signals
two at a time to generate three respective difference signals.
Applying a servo loop to the three mixer circuits to remove
residual carrier signals and long term signal drift therefrom.
Capturing the three respective difference signals. Statistically
analyzing the three difference signals to compute composite power
spectral densities therefor. Computing respective individual power
spectral densities of the first, second and third signals from the
composite power spectral densities to determine the phase noise of
the primary signal source.
The present system and method thus uses mathematical relationships
between three signal sources, with similar magnitudes of phase
noise, to compute the power spectral density of the noise content
of each source. Where traditional techniques typically require
ultrastable reference sources or expensive calibrated delay lines,
the present system requires a minimum of interconnect hardware and
only three inexpensive waveform recorder channels, for example.
Furthermore, the size, weight, and cost of producing the present
phase noise test system is substantially lower than present
commercially available units.
Traditional phase noise measurement techniques typically address
two problem areas: phase noise close to the carrier and phase noise
far from the carrier. Furthermore, because these problems are
diverse, it takes one commercial test set to address each area.
However, the present technique addresses the entire problem with
one system configuration. This has not been done by any commercial
system vendor, and is therefore heretofore unavailable.
Future factory and field testers are expected to be down-sized for
greater portability and lower cost. The present system relies on
virtual instrument concepts to achieve this goal. Virtual
instruments are presently unavailable in the commercial market
place. The present system is adapted to replace existing phase
noise measurement systems currently used for near-in and far-from
the carrier phase measurements. There is also a need for a system
to test devices such as crystal oscillators, synthesizers, atomic
clocks and standards, and low noise and ultra-low noise sources.
The present system makes these tests more affordable while
requiring less space.
BRIEF DESCRIPTION OF THE DRAWINGS
The various features and advantages of the present invention may be
more readily understood with reference to the following detailed
description taken in conjunction with the accompanying drawings,
wherein like reference numerals designate like structural elements,
and in which:
FIG. 1 is a system block diagram of a phase noise measurement
system in accordance with the principles of the present invention;
and
FIG. 2 shows a software flow diagram employed in the phase noise
measurement system of FIG. 1.
DETAILED DESCRIPTION
Referring to the drawing figures, FIG. 1 is a system block diagram
of a phase noise measurement system in accordance with the
principles of the present invention the phase noise measurement
system is comprised of two low noise reference sources 14, 15 that
are both programmable and phase lockable. These reference sources
14, 15 are used in conjunction with a unit under test (UUT) 20 that
comprises a third source. The reference sources 14, 15 are phase
locked to the unit under test 20 by means of first and second phase
locked loops 25, 26. The phase locked loops 25, 26 are adapted to
minimize cancellation of phase noise that is close to the
carrier.
Outputs from the three sources (the two reference sources 14, 15
and the UUT 20) are applied to three splitters 16, 17, 18. The
outputs from the splitters 16, 17, 18 are fed both to three mixers
27, 28, 29 and to three programmable phase shifters 24, 25, 26.
This produces three sets of signals at the mixers 27, 28, 29 that
are in quadrature. However, if the outputs of the mixers 27, 28, 29
produce a resultant signal with a residual carrier, servo
electronics units 21, 22, 23 sweep the phase shifters 24, 25, 26 to
produce a signal with a 0 Hz carrier (null carrier). The outputs of
the three mixers 27, 28, 29 are fed into their respective low pass
filters 31, 32, 33. These filters 31, 32, 33 reject high frequency
mixer products and also limit the noise bandwidth, in that they act
as an anti-aliasing filter. The outputs of the filters 31, 32, 33
are applied to three waveform recorders 11, 12, 13. The waveform
recorders 11, 12, 13 digitize the noise signals and couple them to
a control/process computer 23 for analysis.
FIG. 2 is a block diagram of software 40 employed in the
control/process computer 23 of the phase noise measurement system
10. The software 40 is comprised of eight functional software
routines that process the measured data and produce the desired
spectral results. The first major section of the software 40
comprises a data acquisition module 41. The data acquisition module
41 provides for hardware control of the components of the system 10
and is comprised of two instrument handlers, a waveform recorder
handler 42 for the waveform recorders 11, 12, 13 and a low noise
source handler 43 for the low noise sources 14, 15, 16. The
functions of these handlers 42, 43 include setup of the waveform
recorders 11, 12, 13 and low noise sources 14, 15, 16, and
programming of the phase shifters 24, 25, 26.
Once the data is digitized and acquired by the data acquisition
module 41, control is passed to a phase analysis engine module 44.
In the phase analysis engine module 44, the mixed phase noise data
is analyzed using digital signal processing techniques, including
an autocorrelation generator routine 45, averaging routine 46,
noise extraction routine 48, and a fast Fourier transform routine
47. The autocorrelation generator routine 45 generates
autocorrelation functions for the three mixed and downconverted
noise signals (R13, R23, R12). To minimize the random variance and
quantization noise introduced into the calculations, the
autocorrelation functions for the combined noise signals (R13, R23,
R12) are averaged using the averaging routine 46. The averaging
function involves acquiring multiple signal sets and computing a
series of autocorrelation functions for the combined noise (R13,
R23, R12). Then each autocorrelation function (i.e., R13) is
averaged over the series. Following the averaging calculations for
the combined noise, the autocorrelation functions (R13, R23, R12)
are converted to power spectral density functions (P13, P23, P13)
using the fast Fourier transformation routine 47. The conversion
from autocorrelation functions to power spectral density functions
is based on the Wiener-Khinchin theorem. At this point, the
individual noise power spectral densities (P1, P2, P3) are then
computed using the noise extraction routine 48. The power spectral
densities (P1, P2, P3) are then formatted and passed to a human
interface module 50. The human interface module 50 handles keyboard
interactions, displaying functions, command interpretations,
plotting functions and calculations and the overall process flow of
the software 40. This is accomplished using a process
control/command interpreter routine 51 and a plotting routine
52.
The phase noise measurement system 10 is adapted to measure phase
noise in active devices comprising the unit under test 20 by using
sophisticated digital processing techniques. The novelty of this
system 10 lies in the way the noise signal is handled and analyzed
and the use of autocorrelation functions and spectral power density
functions in the final analysis routines 45-48 of the phase
analysis engine module 44. The value of this technique lies in the
fundamental trade-off between hardware and software, i.e. if
hardware may be eliminated by using software analysis, it lowers
the cost of the measurement system 10. Furthermore, reducing the
hardware results in a lighter, smaller, and more reliable product.
The present system 10 provides a means for addressing factory and
bench testing applications and field testing applications requiring
reduced weight, size, and cost.
The present measurement system 10 trades hardware complexity for
software sophistication. It uses the mathematical relationships
between discrete time measurements, autocorrelation functions,
power spectral density conversions, and error minimization theory
to extract the noise content from a signal derived from the unit
under test 20. To help clarify the principles and theory behind the
present invention, a general mathematical description is presented
below.
To measure the noise content of the signal from the unit under test
20 using the present system 10 requires the two additional
reference sources 14, 15 having substantially the same frequency
and similar noise content. That is, all three sources 14, 15, 20,
have the same carrier frequency and have noise spectra within 10 dB
of each other. Let .phi..sub.1 (t), .phi..sub.2 (t) and .phi..sub.3
(t) represent the phase noise content of each of the three signal
sources 14, 15, 20 and assume that all three sources 14, 15, 20
have carriers in the RF/microwave region. The three signals
produced by the three signal sources 14, 15, 20 are given by:
The next step is to mix the three signals together, two at a time,
to create three new composite baseband signals. Assume that the
mixing is performed with the signals in quadrature and the results
are passed through low pass filters 31, 32, 33 each having a gain
of two. The three sets of signals produced by the three signal
sources 14, 15, 20 are given by:
Then, after filtering:
which correspond to the outputs of the three mixers 27, 28, 29. The
composite noise signals (i.e.: .phi..sub.1 (t)-.phi..sub.3 (t))
represent very small angles (<<.1 radians). Therefore, the
following simplification is made to produce the outputs of the
filters 31, 32, 33:
Since three independent signal sources 14, 15, 20 are provided, it
is also reasonable to assume that the noise content for each source
14, 15, 20 is uncorrelated and that the three baseband composite
noise signals are uncorrelated. In addition, it is assumed that the
three sources 14, 15, 20 produce ergotic random sequences. That is,
the statistics of these random sequences may be determined from a
single collation of observations. If this is role, time averaging
may be substituted for ensemble averaging. Therefore, the auto
correlation functions for the three composite signals may be
computed at follows. The following equations are implemented in the
autocorrelation generator routine 45 of FIG. 2. ##EQU1##
where r.sub.1, r.sub.3 are autocorrelation functions and r.sub.13,
r.sub.31 are cross correlation functions.
However, the phase noise of the three signals are assumed to be
independent. Therefore, all cross correlation functions approach
zero and the autocorrelation function simplifies to:
Extrapolating these results, the autocorrelation functions of the
remaining composite signals are computed in a similar manner.
The last three equations represent the autocorrelation functions
computed by the autocorrelation generator routine 45.
The averaging routine 46 sums a plurality of sets of
autocorrelation signals and divides the resultant sum by the number
of summed sets to produce an average value for the respective
autorrelation functions. This averages out fluctuations in the
signals.
Based on the Wiener-Khinchin Theorem, the Fourier transform of the
autocorrelation function is representative of the power spectral
density. The following equations are implemented in the Fourier
transform routine 48 of FIG. 2.
For this process, a user would be more interested in the power
contained in a frequency interval from 0 to +.infin. and would not
want to distinguish between positive and negative values of
frequency. Therefore, a one sided power spectral density function
may be defined as follows:
From above,
and the Fourier transform of R.sub.13 (t) may be represented as
follows:
Converting the double sided power spectral density to a single
sided representation yields:
Therefore, extrapolating these results for all three power spectral
densities results in the following equations:
With these three equations, it is possible to solve for the
individual power spectral densities of the three sources 14, 15, 20
which are represented by P.sub.1 (f), P.sub.2 (f), and P.sub.3 (f)
as a function of the measured composite power spectral densities
represented by P.sub.13 (f), P.sub.12 (f), and P.sub.23 (f). This
is implemented in the noise extraction routine 48.
The mathematical description presented above is for a continuous
systems case. However, to implement a real world instrument to
perform this process requires discretization and complex digital
signal processing. Therefore, it is important to validate the
process for systems that are characterized by discrete data
samples.
Let .phi..sub.1 (n.DELTA.t), .phi..sub.2 (n.DELTA.t), and
.phi..sub.3 (n.DELTA.t) represent the discretely sampled phase
noise content of the three discrete signal sources 14, 15, 20.
These signal sources 14, 15, 20 (and their noise content) were
originally continuous signals that were mixed down to 0 Hz baseband
and then digitized. The mixing process is the same as described
above.
The composite noise signals (i.e.,: .phi..sub.1
(n.DELTA.t)-.phi..sub.3 (n.DELTA.t)) represent very small angles
(<<.1 radians). Therefore, the following simplification is
made:
Since these are three independent signal sources 14, 15, 20, it is
also reasonable to assume that their noise content are also
uncorrelated. It is also assumed that the three sources 14, 15, 20
produce a discrete ergotic random sequence. Therefore, the discrete
autocorrelation functions for the three composite signals may be
computed as follows: ##EQU2## Expanding the discrete
autocorrelation functions and evaluating them results in:
##EQU3##
If the signals .phi..sub.1 (n.DELTA.t), .phi..sub.2 (n.DELTA.t),
and .phi..sub.3 (n.DELTA.t) are independent and uncorrelated, then
their discrete cross-correlation functions should approach zero.
Therefore:
Based on the Wiener-Khinchin Theorem, the discrete Fourier
transform of the discrete autocorrelation function is
representative of the power spectral density. ##EQU4## and
However, it should also be noted that an alternative method for
calculating discrete power spectral density functions is to use a
periodogram method. The periodogram method is implemented by taking
a discrete Fourier transform of the discrete time domain signal and
then computing the power spectral density. This is performed in the
fast Fourier transform routine 47. ##EQU5##
The discrete power spectral density functions are then converted to
a one-sided power spectrum in the same manner as described
above.
At this point, there are three equations in three unknowns and all
of the individual power spectral densities can be solved for.
Thus there has been described a new and improved phase noise
measurement system that uses three independent signal sources to
statistically derive the power spectral density of the phase noise
content of each of them. It is to be understood that the
above-described embodiment is merely illustrative of some of the
many specific embodiments which represent applications of the
principles of the present invention. Clearly, numerous and other
arrangements can be readily devised by those skilled in the art
without departing from the scope of the invention.
* * * * *